Section B: Information Matching
Directions: In this section, you are going to read a passage with ten statements attached to it. Each statement contains information given in one of the paragraphs. Identify the paragraph from which the information is derived. You may choose a paragraph more than once. Each paragraph is marked with a letter. Answer the questions by marking the corresponding letter on Answer Sheet 2.
The Challenges for Artificial Intelligence in Agriculture
[A] A group of corn farmers stands huddled around an agronomist and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
[B] The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
[C] In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
[D] At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
[E] The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
[F] As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
[G] Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
[H] Remote sensors enable algorithms to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
[I] In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
[J] Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
[K] So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
[L] By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
[M] What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
[N] So the problem with deploying machine learning and artificial intelligence in agriculture is not that scientists lack the capacity to develop programs and protocols to begin to address the biggest of growers’ concerns; the problem is that in most cases, no two environments will be exactly alike, which makes the testing, validation and successful rollout of such technologies much more laborious than in most other industries.
[O] Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environment is to basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
[P] Backed by the venture capital community, which is now investing billions of dollars in the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
[Q] This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.
[R] There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
36. Farmers will not profit from replanting once they have applied most of the fertilizer and other chemicals to their fields.
37. Agriculture differs from the medical science of the human body in that its environment is not a contained one.
38. The agronomist is sure that he will obtain a near accurate count of plant population with his software.
39. The application of artificial intelligence to agriculture is much more challenging than to most other industries.
40. Even the farmers know the data provided by the UAV is not correct.
41. The pressure for quick results leads to product failure, which, in turn, arouses doubts about the applicability of AI technology to agriculture.
42. Remote sensors are aimed to help farmers improve decision-making to increase yields.
43. The farmer expects the software to tell him whether he will have to replant any parts of his farm fields.
44. Agriculture proves very difficult to quantify because of the constantly changing conditions involved.
45. The same seed and fertilizer program may yield completely different outcomes in different places.
Answers & Explanations (答案与解析)
36. D。解析：题干意为“一旦农民在田地里施用了大部分肥料和其他化学物质，他们就不会从重新种植中获利”。对应 [D] 段的 “...before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action...”（……在他的大部分肥料和化学应用需要进行之前。一旦应用了这些，采取纠正措施（重植）在经济上就不可行了）。economically unviable 对应 will not profit。
37. L。解析：题干意为“农业与人体的医学科学不同，因为它的环境不是一个受控的环境”。对应 [L] 段的 “By comparison, our bodies are a contained environment... But these ecosystems (Agriculture) are not contained.”（相比之下，我们的身体是一个受控的环境……但这些生态系统（农业）是不受控制的）。not a contained one 对应 are not contained。
38. E。解析：题干意为“农学家确信他将通过他的软件获得几乎准确的植物数量”。对应 [E] 段最后一句：“...the agronomist feels confident that the software will produce a near accurate result.”（……农学家确信该软件将产生近乎准确的结果）。is sure 对应 feels confident。
39. N。解析：题干意为“人工智能在农业中的应用比在大多数其他行业中更具挑战性”。对应 [N] 段最后一句：“...no two environments will be exactly alike, which makes the testing, validation and successful rollout of such technologies much more laborious than in most other industries.”（……没有两个环境会完全相同，这使得此类技术的测试、验证和成功推广比在大多数其他行业中费力得多）。challenging 对应 laborious。
40. F。解析：题干意为“甚至连农民都知道无人机提供的数据不正确”。对应 [F] 段最后一句：“...he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers...”（……他在屏幕上看到数据的那一刻就知道植物数量不正确，农民们也知道……）。so do the farmers 对应 Even the farmers know。
41. Q。解析：题干意为“要求快速出结果的压力导致了产品失败，这反过来又引起了人们对人工智能技术在农业适用性的怀疑”。对应 [Q] 段首句（承接 P 段被迫尽快完成开发）：“This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology.”（这通常会导致产品失败，从而引起市场的怀疑，并对机器学习技术的完整性造成打击）。arouses doubts 对应 leads to skepticism。
42. H。解析：题干意为“遥感器旨在帮助农民改善决策以提高产量”。对应 [H] 段最后一句：“And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.”（目标是农民可以利用这种人工智能，通过在田间做出更好的决定来实现获得更好收成的目标）。increase yields 对应 a better harvest。
43. C。解析：题干意为“农民期望软件告诉他，他是否必须在他的农田的任何部分进行重新种植”。对应 [C] 段的 “...the farmers and agronomist are looking to specialized software... the farmer wants to determine if there are any parts of the field that require replanting...”（……农民和农学家指望专门的软件……农民想确定田地里是否有任何部分需要重新种植……）。tell him whether he will have to replant 对应 determine if there are any parts of the field that require replanting。
44. K。解析：题干意为“农业被证明很难量化，因为它涉及不断变化的条件”。对应 [K] 段的 “Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing...”（因为为了统计量化的目的，它是最难控制的领域之一。即使在同一块地里，条件也总是在变化……）。difficult to quantify 对应 difficult fields to contain for the purpose of statistical quantification。
45. M。解析：题干意为“相同的种子和肥料计划可能会在不同地方产生完全不同的结果”。对应 [M] 段的 “What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa.”（在美国中西部地区使用相同种子和肥料计划可能发生的情况，几乎肯定与在澳大利亚或南非使用相同种子和肥料计划可能发生的情况无关）。completely different outcomes 对应 unrelated to what may occur。
核心搭配与高分句型
【核心搭配与高频短语】
take off：起飞（that takes off and lands using propellers）
address concerns/abnormalities：处理问题/异常情况（to address, almost immediately, any crop abnormalities）
look to：指望，依赖（are looking to specialized software）
subject to：受...支配，容易遭受（They are subject to climatic occurrences）
take into account：考虑到，顾及（taking literally many hundreds... factors into account）
deliver a blow to：对...造成打击（delivers a blow to the integrity of...）
【亮点句型解析】
It is difficult to grasp just how..., without understanding that... 双重否定/强调句型（E段）：
"It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days..."
（如果不知道仅仅一年多前还需要三到五天的话，就很难理解这是多么令人印象深刻……）通过“如果不……就很难……”的结构，强烈突出了技术进步之快。
"It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days..."
（如果不知道仅仅一年多前还需要三到五天的话，就很难理解这是多么令人印象深刻……）通过“如果不……就很难……”的结构，强烈突出了技术进步之快。
Having walked... 现在分词完成式作状语（F段）：
"Having walked through the planted rows before the flight to gain a physical understanding... he knows the instant he sees the data..."
（由于在飞行前走过了种植的行距以获得了对地面情况的物理了解，他在看到数据的瞬间就知道……）`Having done` 结构清晰地表达了状语动作发生在主句动作（knows）之前。
"Having walked through the planted rows before the flight to gain a physical understanding... he knows the instant he sees the data..."
（由于在飞行前走过了种植的行距以获得了对地面情况的物理了解，他在看到数据的瞬间就知道……）`Having done` 结构清晰地表达了状语动作发生在主句动作（knows）之前。